These kinds of experiences are difficult to narrativize. There is no story arc. In “On Being Ill,” Virginia Woolf writes:

Considering how common illness is, how tremendous the spiritual change that it brings, how astonishing, when the lights of health go down, the undiscovered countries that are then disclosed, what wastes and deserts of the soul a slight attack of influenza brings to light…it becomes strange indeed that illness has not taken its place with love, battle, and jealousy among the prime themes of literature. Novels, one would have thought, would have been devoted to influenza; epic poems to typhoid; odes to pneumonia… But no; … literature does its best to maintain that its concern is with the mind; that the body is a sheet of plain glass through which the soul looks straight and clear.

This is, of course, the romantic view. Sometimes it’s not true; sometimes I’m the same asshole I was before I got sick. But as Susan Sontag once wrote, “illness exacerbates consciousness.” As such, my life has been irrevocably changed by the experience of illness. There is a lot of shame associated with disease. Disease is not polite conversation, and at my age, a career—not wellness—is the expected goal.

I give voice to this period of my life not as an inconvenient period, but as a profound one worthy of being shared. I want to valorize my time in ways that have nothing to do with work, to say a big “fuck you” to every person at a dinner party who has ever pointedly asked me, “So…what do you do?” because I haven’t “done” much in a long time. The story I’m telling here is equal parts a processing of the trauma of illness and an exploration of how the body is treated under the regime of capitalism. Stories of illness like mine should not be kept away in beds and in hospital wards. They should be written so that we can understand the body as something beyond a sheet of plain glass.

Theoretical computer science has its uses and applications and can turn out to be quite practical. In this article, targeted at programmers who know their art but who don’t have any theoretical computer science background, I will present one of the most pragmatic tools of computer science: Big O notation and algorithm complexity analysis. As someone who has worked both in a computer science academic setting and in building production-level software in the industry, this is the tool I have found to be one of the truly useful ones in practice, so I hope after reading this article you can apply it in your own code to make it better. After reading this post, you should be able to understand all the common terms computer scientists use such as "big O", "asymptotic behavior" and "worst-case analysis".